scispace - formally typeset
Search or ask a question
Journal Article

Fingerprint Image Enhancement and Minutiae Extraction

TL;DR: Presents a fast fingerprint enhancement and minutiae extraction algorithm which improves the clarity of the ridge and valley structures of the input fingerprint images based on the frequency and orientation of the local ridges and thereby extracts correctminutiae.
Abstract: Automatic and reliable extraction of minutiae from fingerprint images is a critical step in fingerprint matching.The quality of input fingerprint images plays an important role in the performance of automatic identification and verification algorithms.Presents a fast fingerprint enhancement and minutiae extraction algorithm which improves the clarity of the ridge and valley structures of the input fingerprint images based on the frequency and orientation of the local ridges and thereby extracts correct minutiae.Experimental results show that the method performs well.
Citations
More filters
Journal ArticleDOI
TL;DR: The results proved that the used software functioned perfectly until a compression ratio of (30–40%) of the raw images; any higher ratio would negatively affect the accuracy of the used system.
Abstract: Despite the large body of work on fingerprint identification systems, most of it focused on using specialized devices. Due to the high price of such devices, some researchers directed their attention to digital cameras as an alternative source for fingerprints images. However, such sources introduce new challenges related to image quality. Specifically, most digital cameras compress captured images before storing them leading to potential losses of information. This study comes to address the need to determine the optimum ratio of the fingerprint image compression to ensure the fingerprint identification system’s high accuracy. This study is conducted using a large in-house dataset of raw images. Therefore, all fingerprint information is stored in order to determine the compression ratio accurately. The results proved that the used software functioned perfectly until a compression ratio of (30–40%) of the raw images; any higher ratio would negatively affect the accuracy of the used system.

154 citations


Cites methods from "Fingerprint Image Enhancement and M..."

  • ...This technique is denoted by Crossing Number (CN), and it is applied commonly to extract the minutiae [53, 54]....

    [...]

Journal ArticleDOI
TL;DR: The results show that the modified sub-models of an existing mathematical algorithm for the fingerprint image enhancement perform well with significant improvement over the original versions and the necessity of each level of the enhancement.
Abstract: Fingerprint has remained a very vital index for human recognition. In the field of security, series of Automatic Fingerprint Identification Systems (AFIS) have been developed. One of the indices for evaluating the contributions of these systems to the enforcement of security is the degree with which they appropriately verify or identify input fingerprints. This degree is generally determined by the quality of the fingerprint images and the efficiency of the algorithm. In this paper, some of the sub-models of an existing mathematical algorithm for the fingerprint image enhancement were modified to obtain new and improved versions. The new versions consist of different mathematical models for fingerprint image segmentation, normalization, ridge orientation estimation, ridge frequency estimation, Gabor filtering, binarization and thinning. The implementation was carried out in an environment characterized by Window Vista Home Basic operating system as platform and Matrix Laboratory (MatLab) as frontend engine. Synthetic images as well as real fingerprints obtained from the FVC2004 fingerprint database DB3 set A were used to test the adequacy of the modified sub-models and the resulting algorithm. The results show that the modified sub-models perform well with significant improvement over the original versions. The results also show the necessity of each level of the enhancement. Keyword- AFIS; Pattern recognition; pattern matching; fingerprint; minutiae; image enhancement.

48 citations


Cites background or methods from "Fingerprint Image Enhancement and M..."

  • ...A block processing approach used in [8]-[9] is adopted in this research for obtaining the grey-level variance values....

    [...]

  • ...The clear separation of the ridges from the valleys verifies the correctness of the algorithm as proposed in [9] and implemented in this research....

    [...]

  • ...c) The local orientation of a pixel in a fingerprint image was computed by using its S x S neighborhood in [8]-[9]....

    [...]

  • ...This paper adopts with slight modifications, the algorithm implemented in [8][9] for fingerprint image enhancement....

    [...]

  • ...The first of the tasks of image normalization implemented in [8]-[9] and adopted for this research is the division of the segmented image into blocks of size S x S....

    [...]

Book ChapterDOI
01 Jan 2009
TL;DR: The aim of this research is to establish a relationship between gender and the fingerprint using some special features such as ridge density, ridge thickness to valley thickness ratio (RTVTR) and ridge width, and found male-female can be correctly classified upto 91%.
Abstract: Male-female classification from a fingerprint is an important step in forensic science, anthropological and medical studies to reduce the efforts required for searching a person. The aim of this research is to establish a relationship between gender and the fingerprint using some special features such as ridge density, ridge thickness to valley thickness ratio (RTVTR) and ridge width. Ahmed Badawi et. al. showed that male-female classification can be done correctly upto 88.5% based on white lines count, RTVTR & ridge count using Neural Network as Classifier. We have used RTVTR, ridge width and ridge density for classification and SVM as classifier. We have found male-female can be correctly classified upto 91%.

30 citations


Cites methods from "Fingerprint Image Enhancement and M..."

  • ...Normalization is used to standardize the intensity values of an image by adjusting the range of its grey-level values so that they lie within a desired range of values e.g. zero mean and unit standard deviation....

    [...]

  • ...The main steps of the algorithm are: Normalization [4] Normalization is used to standardize the intensity values of an image by adjusting the range of its grey-level values so that they lie within a desired range of values e....

    [...]

Book ChapterDOI
27 Aug 2007
TL;DR: Two new normalization techniques Four Segments Piecewise Linear (FSPL) and Linear Tanh Linear (LTL) have been proposed and they perform better and particularly, LTL normalization is efficient and robust.
Abstract: This paper attempts to make an quantitative evaluation of available normalization techniques of matching scores in multimodal biometric systems. Two new normalization techniques Four Segments Piecewise Linear (FSPL) and Linear Tanh Linear (LTL) have been proposed in this paper. FSPL normalization techniques divides the region of genuine and impostor scores into four segments and maps each segment using piecewise linear function while LTL normalization techniques maps the non-overlap region of genuine and impostor score distributions to a constant function and overlap region using tanh estimator. The effectiveness of each technique is shown using EER and ROC curves on IITK database of having more than 600 people on following characteristics: face, fingerprint, and offline-signature. The proposed normalization techniques perform better and particularly, LTL normalization is efficient and robust.

27 citations


Cites methods from "Fingerprint Image Enhancement and M..."

  • ...In this paper matching scores of face and fingerprint characteristics are obtained using Haar wavelet [4] and minutiae based technique [5], respecively while global and local features are used to compute the matching scores for offline-signature [6]....

    [...]

Proceedings ArticleDOI
08 Dec 2011
TL;DR: A new algorithm based on minutiae extraction which is inspired by currently fingerprint systems, but adapted to the own characteristics of vein patterns is described, including also the biometric feature extraction process.
Abstract: In this paper, the authors will describe a new algorithm based on minutiae extraction which is inspired by currently fingerprint systems, but adapted to the own characteristics of vein patterns. All the steps of the system, from the image pre-processing to the comparison algorithm, are described, including also the biometric feature extraction process. After describing the system, some obtained results are detailed. The algorithm proposed has been tested with two different databases: one database acquired by the authors, using a self-designed sensor; and one semi-publicly accessible database. These results do not only show the low error rates obtained but also the universality of the proposed system, as well as the ease of adapting the algorithm to cope with the different characteristics of each database

26 citations


Cites background from "Fingerprint Image Enhancement and M..."

  • ...Ridge endings are points where the ridge curve terminates, and bifurcations are points where a ridge splits from a single path into two paths at a Yjunction [8]....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: The results proved that the used software functioned perfectly until a compression ratio of (30–40%) of the raw images; any higher ratio would negatively affect the accuracy of the used system.
Abstract: Despite the large body of work on fingerprint identification systems, most of it focused on using specialized devices. Due to the high price of such devices, some researchers directed their attention to digital cameras as an alternative source for fingerprints images. However, such sources introduce new challenges related to image quality. Specifically, most digital cameras compress captured images before storing them leading to potential losses of information. This study comes to address the need to determine the optimum ratio of the fingerprint image compression to ensure the fingerprint identification system’s high accuracy. This study is conducted using a large in-house dataset of raw images. Therefore, all fingerprint information is stored in order to determine the compression ratio accurately. The results proved that the used software functioned perfectly until a compression ratio of (30–40%) of the raw images; any higher ratio would negatively affect the accuracy of the used system.

154 citations

Journal ArticleDOI
TL;DR: The results show that the modified sub-models of an existing mathematical algorithm for the fingerprint image enhancement perform well with significant improvement over the original versions and the necessity of each level of the enhancement.
Abstract: Fingerprint has remained a very vital index for human recognition. In the field of security, series of Automatic Fingerprint Identification Systems (AFIS) have been developed. One of the indices for evaluating the contributions of these systems to the enforcement of security is the degree with which they appropriately verify or identify input fingerprints. This degree is generally determined by the quality of the fingerprint images and the efficiency of the algorithm. In this paper, some of the sub-models of an existing mathematical algorithm for the fingerprint image enhancement were modified to obtain new and improved versions. The new versions consist of different mathematical models for fingerprint image segmentation, normalization, ridge orientation estimation, ridge frequency estimation, Gabor filtering, binarization and thinning. The implementation was carried out in an environment characterized by Window Vista Home Basic operating system as platform and Matrix Laboratory (MatLab) as frontend engine. Synthetic images as well as real fingerprints obtained from the FVC2004 fingerprint database DB3 set A were used to test the adequacy of the modified sub-models and the resulting algorithm. The results show that the modified sub-models perform well with significant improvement over the original versions. The results also show the necessity of each level of the enhancement. Keyword- AFIS; Pattern recognition; pattern matching; fingerprint; minutiae; image enhancement.

48 citations

Proceedings Article
01 Aug 2011
TL;DR: Experiments on the WVU and FVC2000 datasets show that the Mixed fingerprint can potentially be used for authentication and that the identity of the original fingerprint cannot be easily deduced from the mixed fingerprint.
Abstract: Securing a stored fingerprint image is of paramount importance because a compromised fingerprint cannot be easily revoked. In this work, an input fingerprint image is mixed with another fingerprint (e.g., from a different finger), in order to produce a new mixed image that obscures the identity of the original fingerprint. Mixing fingerprints creates a new entity that looks like a plausible fingerprint and, thus, (a) it can be processed by conventional fingerprint algorithms and (b) an intruder may not be able to determine if a given print is mixed or not. To mix two fingerprints, each fingerprint is decomposed into two components, viz., the continuous and spiral components. After pre-aligning the two components of each fingerprint, the continuous component of one fingerprint is combined with the spiral component of the other fingerprint image in order to generate a mixed fingerprint. Experiments on the WVU and FVC2000 datasets show that the mixed fingerprint can potentially be used for authentication and that the identity of the original fingerprint cannot be easily deduced from the mixed fingerprint. Further, the mixed fingerprint can facilitate in the generation of cancelable templates.

36 citations

Proceedings ArticleDOI
29 Nov 2011
TL;DR: Experiments on a subset of the WVU fingerprint dataset show that the proposed approach can be used to generate virtual identities from images of two different fingers pertaining to a single individual or different individuals.
Abstract: This work explores the possibility of mixing two different fingerprints at the image level in order to generate a new fingerprint. To mix two fingerprints, each fingerprint is decomposed into two different components, viz., the continuous and spiral components. After pre-aligning the components of each fingerprint, the continuous component of one fingerprint is combined with the spiral component of the other fingerprint image. Experiments on a subset of the WVU fingerprint dataset show that the proposed approach can be used to generate virtual identities from images of two different fingers pertaining to a single individual or different individuals.

33 citations

Book ChapterDOI
01 Jan 2009
TL;DR: The aim of this research is to establish a relationship between gender and the fingerprint using some special features such as ridge density, ridge thickness to valley thickness ratio (RTVTR) and ridge width, and found male-female can be correctly classified upto 91%.
Abstract: Male-female classification from a fingerprint is an important step in forensic science, anthropological and medical studies to reduce the efforts required for searching a person. The aim of this research is to establish a relationship between gender and the fingerprint using some special features such as ridge density, ridge thickness to valley thickness ratio (RTVTR) and ridge width. Ahmed Badawi et. al. showed that male-female classification can be done correctly upto 88.5% based on white lines count, RTVTR & ridge count using Neural Network as Classifier. We have used RTVTR, ridge width and ridge density for classification and SVM as classifier. We have found male-female can be correctly classified upto 91%.

30 citations